Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fbf4573da58>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fbf45674d68>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [14]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name="real_inputs")
    z_inputs = tf.placeholder(tf.float32, (None, z_dim), name="z_inputs")
    learning_rate = tf.placeholder(tf.float32)
    return real_inputs, z_inputs, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [15]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    num_kernels = 32
    kernel_size = 5
    kernel_stride = 2
    alpha = 0.2
    with tf.variable_scope("discriminator", reuse=reuse):
        
        # First Convoluational Layer with Leaky ReLu
        # input is 28x28x1 or 28x28x3
        x1 = tf.layers.conv2d(images, num_kernels, kernel_size, strides=kernel_stride, padding='SAME')
        x1 = tf.maximum(alpha*x1, x1)
        
        # Second Convoluational Layer with Batch Normalization and Leaky ReLu
        # input 14x14x32
        x2 = tf.layers.conv2d(x1, 2*num_kernels, kernel_size, strides=kernel_stride, padding='SAME')
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(alpha*x2, x2)
        
        # Third Convoluational Layer with Batch Normalization and Leaky ReLu
        # input 7x7x64
        x3 = tf.layers.conv2d(x2, 4*num_kernels, kernel_size, strides=kernel_stride, padding='SAME')
        x3 = tf.layers.batch_normalization(x3, training=True)
        x3 = tf.maximum(alpha*x3, x3)
        
        # Flatten and Fully-Connected Output Layers
        flat = tf.contrib.layers.flatten(x3)
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [16]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    num_kernels = 512
    kernel_size = 3
    kernel_stride = 2
    alpha = 0.2
    reuse = not is_train
    with tf.variable_scope("generator", reuse=reuse):
        
        # Reshape input to z dimension input
        x = tf.layers.dense(z, 7 * 7 * num_kernels)
        x = tf.reshape(x, (-1, 7, 7, num_kernels))
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x, x)
        
        # First Transpose Convolutional Layer
        # input: 7x7x512
        x1 = tf.layers.conv2d_transpose(x, num_kernels//2, kernel_size, strides=kernel_stride, padding='SAME')
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha*x1, x1)
    
        # Second Transpose Convolutional Layer
        # input: 14x14x256
        x2 = tf.layers.conv2d_transpose(x1, num_kernels//2, kernel_size, strides=kernel_stride, padding='SAME')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha*x2, x2)
        
        # Third Transpose Convolutional Layer
        # input: 28x28x128
        x3 = tf.layers.conv2d_transpose(x2, num_kernels//2, kernel_size, strides=1, padding='SAME')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha*x3, x3)
    
        # Fourth Convolutional Layer
        # input: 28x28x64
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, kernel_size, strides=1, padding='SAME')
        out = tf.tanh(logits)
        
    return out 


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [17]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # Generator image
    g_image = generator(input_z, out_channel_dim, is_train=True)

    # Discriminator output on real and fake images
    d_out_real, d_logits_real = discriminator(input_real)
    d_out_fake, d_logits_fake = discriminator(g_image, reuse=True) 
    
    # Discriminator loss on real and fake images
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.zeros_like(d_out_fake)))
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_real, labels=tf.ones_like(d_out_real)*0.9))

    # Discriminator total loss
    d_loss = d_loss_fake + d_loss_real
    
    # Generator loss
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [18]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    train_vars = tf.trainable_variables()
    g_vars = [var for var in train_vars if var.name.startswith('generator')]
    d_vars = [var for var in train_vars if var.name.startswith('discriminator')]
    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    return d_opt, g_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [19]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    tf.reset_default_graph()
    real_input, z_input, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(real_input, z_input, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        training_step = 0
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # Step counter
                training_step += 1
                
                # Sample random noise for generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run Training operations
                _ = sess.run(d_opt, feed_dict={real_input: batch_images, z_input: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={real_input: batch_images, z_input:batch_z, lr:learning_rate})

                if training_step % 100 == 0:
                    
                    # Print Losses
                    train_loss_d = d_loss.eval({real_input: batch_images, z_input: batch_z, lr: learning_rate})
                    train_loss_g = g_loss.eval({real_input: batch_images, z_input: batch_z, lr: learning_rate})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                
                    
                    # Sample random noise for generator
                    batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                    
                    # Show generator output
                    show_generator_output(sess, 10, z_input, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [64]:
batch_size = 10
z_dim = 50
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 2.0309... Generator Loss: 0.3776
Epoch 1/2... Discriminator Loss: 1.1542... Generator Loss: 0.7691
Epoch 1/2... Discriminator Loss: 0.5482... Generator Loss: 1.8400
Epoch 1/2... Discriminator Loss: 0.3764... Generator Loss: 4.0523
Epoch 1/2... Discriminator Loss: 1.4204... Generator Loss: 4.9113
Epoch 1/2... Discriminator Loss: 1.3990... Generator Loss: 0.4509
Epoch 1/2... Discriminator Loss: 0.3796... Generator Loss: 3.4553
Epoch 1/2... Discriminator Loss: 0.9314... Generator Loss: 0.8781
Epoch 1/2... Discriminator Loss: 0.4400... Generator Loss: 2.7947
Epoch 1/2... Discriminator Loss: 0.6311... Generator Loss: 1.6205
Epoch 1/2... Discriminator Loss: 0.7555... Generator Loss: 1.4444
Epoch 1/2... Discriminator Loss: 0.3665... Generator Loss: 4.1630
Epoch 1/2... Discriminator Loss: 0.3590... Generator Loss: 5.3065
Epoch 1/2... Discriminator Loss: 0.5422... Generator Loss: 1.7967
Epoch 1/2... Discriminator Loss: 0.4638... Generator Loss: 2.2169
Epoch 1/2... Discriminator Loss: 0.4316... Generator Loss: 3.5120
Epoch 1/2... Discriminator Loss: 0.3855... Generator Loss: 6.8595
Epoch 1/2... Discriminator Loss: 0.3444... Generator Loss: 5.5823
Epoch 1/2... Discriminator Loss: 0.3453... Generator Loss: 5.0755
Epoch 1/2... Discriminator Loss: 1.1476... Generator Loss: 0.6170
Epoch 1/2... Discriminator Loss: 0.3827... Generator Loss: 3.5582
Epoch 1/2... Discriminator Loss: 0.3921... Generator Loss: 4.6158
Epoch 1/2... Discriminator Loss: 0.4369... Generator Loss: 2.6315
Epoch 1/2... Discriminator Loss: 0.5893... Generator Loss: 1.5872
Epoch 1/2... Discriminator Loss: 0.6846... Generator Loss: 1.6200
Epoch 1/2... Discriminator Loss: 0.5251... Generator Loss: 2.0935
Epoch 1/2... Discriminator Loss: 0.8180... Generator Loss: 1.0904
Epoch 1/2... Discriminator Loss: 0.3896... Generator Loss: 3.6227
Epoch 1/2... Discriminator Loss: 0.3916... Generator Loss: 3.5848
Epoch 1/2... Discriminator Loss: 0.9447... Generator Loss: 1.0466
Epoch 1/2... Discriminator Loss: 0.3609... Generator Loss: 7.6662
Epoch 1/2... Discriminator Loss: 0.5250... Generator Loss: 2.2626
Epoch 1/2... Discriminator Loss: 0.5789... Generator Loss: 1.6628
Epoch 1/2... Discriminator Loss: 0.6141... Generator Loss: 1.6586
Epoch 1/2... Discriminator Loss: 0.4414... Generator Loss: 2.7763
Epoch 1/2... Discriminator Loss: 0.7244... Generator Loss: 1.4109
Epoch 1/2... Discriminator Loss: 0.6516... Generator Loss: 1.6489
Epoch 1/2... Discriminator Loss: 0.5833... Generator Loss: 3.3261
Epoch 1/2... Discriminator Loss: 0.3630... Generator Loss: 4.8844
Epoch 1/2... Discriminator Loss: 0.6233... Generator Loss: 1.7718
Epoch 1/2... Discriminator Loss: 0.7322... Generator Loss: 2.0493
Epoch 1/2... Discriminator Loss: 0.5017... Generator Loss: 3.5604
Epoch 1/2... Discriminator Loss: 0.5287... Generator Loss: 1.9720
Epoch 1/2... Discriminator Loss: 0.6217... Generator Loss: 2.2052
Epoch 1/2... Discriminator Loss: 0.3688... Generator Loss: 4.3153
Epoch 1/2... Discriminator Loss: 0.6474... Generator Loss: 1.5878
Epoch 1/2... Discriminator Loss: 0.5771... Generator Loss: 2.2612
Epoch 1/2... Discriminator Loss: 0.6842... Generator Loss: 2.4699
Epoch 1/2... Discriminator Loss: 0.4683... Generator Loss: 2.4148
Epoch 1/2... Discriminator Loss: 1.5613... Generator Loss: 0.4047
Epoch 1/2... Discriminator Loss: 0.6250... Generator Loss: 1.8129
Epoch 1/2... Discriminator Loss: 0.4865... Generator Loss: 2.5271
Epoch 1/2... Discriminator Loss: 0.4338... Generator Loss: 2.5989
Epoch 1/2... Discriminator Loss: 0.4512... Generator Loss: 2.7282
Epoch 1/2... Discriminator Loss: 0.4753... Generator Loss: 2.3158
Epoch 1/2... Discriminator Loss: 0.8212... Generator Loss: 1.1303
Epoch 1/2... Discriminator Loss: 0.3731... Generator Loss: 4.5682
Epoch 1/2... Discriminator Loss: 0.5297... Generator Loss: 3.3788
Epoch 1/2... Discriminator Loss: 0.5337... Generator Loss: 1.9951
Epoch 1/2... Discriminator Loss: 0.7342... Generator Loss: 1.2530
Epoch 2/2... Discriminator Loss: 1.1472... Generator Loss: 0.6733
Epoch 2/2... Discriminator Loss: 0.4878... Generator Loss: 2.0839
Epoch 2/2... Discriminator Loss: 0.8707... Generator Loss: 1.1092
Epoch 2/2... Discriminator Loss: 0.8354... Generator Loss: 1.1766
Epoch 2/2... Discriminator Loss: 0.6068... Generator Loss: 1.6263
Epoch 2/2... Discriminator Loss: 0.5024... Generator Loss: 4.7405
Epoch 2/2... Discriminator Loss: 0.5176... Generator Loss: 2.1474
Epoch 2/2... Discriminator Loss: 0.5553... Generator Loss: 1.9827
Epoch 2/2... Discriminator Loss: 0.9635... Generator Loss: 0.9177
Epoch 2/2... Discriminator Loss: 0.7157... Generator Loss: 1.5638
Epoch 2/2... Discriminator Loss: 0.6459... Generator Loss: 1.6121
Epoch 2/2... Discriminator Loss: 0.4261... Generator Loss: 4.2548
Epoch 2/2... Discriminator Loss: 0.4837... Generator Loss: 2.0570
Epoch 2/2... Discriminator Loss: 0.4726... Generator Loss: 2.3867
Epoch 2/2... Discriminator Loss: 0.5886... Generator Loss: 1.6887
Epoch 2/2... Discriminator Loss: 2.4391... Generator Loss: 0.1464
Epoch 2/2... Discriminator Loss: 0.8392... Generator Loss: 1.0761
Epoch 2/2... Discriminator Loss: 0.4408... Generator Loss: 2.5038
Epoch 2/2... Discriminator Loss: 0.5172... Generator Loss: 1.9485
Epoch 2/2... Discriminator Loss: 0.6621... Generator Loss: 1.5380
Epoch 2/2... Discriminator Loss: 0.8246... Generator Loss: 1.3729
Epoch 2/2... Discriminator Loss: 0.6968... Generator Loss: 1.3101
Epoch 2/2... Discriminator Loss: 0.4407... Generator Loss: 2.6400
Epoch 2/2... Discriminator Loss: 0.5603... Generator Loss: 2.0343
Epoch 2/2... Discriminator Loss: 1.0875... Generator Loss: 0.7041
Epoch 2/2... Discriminator Loss: 0.9715... Generator Loss: 0.9332
Epoch 2/2... Discriminator Loss: 0.3588... Generator Loss: 5.5962
Epoch 2/2... Discriminator Loss: 0.6916... Generator Loss: 1.4195
Epoch 2/2... Discriminator Loss: 0.4409... Generator Loss: 3.0714
Epoch 2/2... Discriminator Loss: 0.5771... Generator Loss: 1.8954
Epoch 2/2... Discriminator Loss: 0.6527... Generator Loss: 2.5508
Epoch 2/2... Discriminator Loss: 0.6329... Generator Loss: 1.4843
Epoch 2/2... Discriminator Loss: 0.6898... Generator Loss: 1.5325
Epoch 2/2... Discriminator Loss: 0.6660... Generator Loss: 2.9611
Epoch 2/2... Discriminator Loss: 0.5288... Generator Loss: 2.2057
Epoch 2/2... Discriminator Loss: 0.4541... Generator Loss: 2.8684
Epoch 2/2... Discriminator Loss: 0.9588... Generator Loss: 0.9667
Epoch 2/2... Discriminator Loss: 0.6288... Generator Loss: 1.8036
Epoch 2/2... Discriminator Loss: 0.5406... Generator Loss: 1.9115
Epoch 2/2... Discriminator Loss: 0.4628... Generator Loss: 2.9608
Epoch 2/2... Discriminator Loss: 0.7730... Generator Loss: 1.5714
Epoch 2/2... Discriminator Loss: 0.4354... Generator Loss: 2.8978
Epoch 2/2... Discriminator Loss: 0.7401... Generator Loss: 1.2883
Epoch 2/2... Discriminator Loss: 0.4251... Generator Loss: 3.5749
Epoch 2/2... Discriminator Loss: 0.9175... Generator Loss: 1.0889
Epoch 2/2... Discriminator Loss: 0.8976... Generator Loss: 1.2218
Epoch 2/2... Discriminator Loss: 0.4361... Generator Loss: 3.5096
Epoch 2/2... Discriminator Loss: 0.5558... Generator Loss: 2.0398
Epoch 2/2... Discriminator Loss: 0.4301... Generator Loss: 4.6594
Epoch 2/2... Discriminator Loss: 0.6129... Generator Loss: 1.7207
Epoch 2/2... Discriminator Loss: 0.6990... Generator Loss: 1.7905
Epoch 2/2... Discriminator Loss: 0.4915... Generator Loss: 2.9549
Epoch 2/2... Discriminator Loss: 0.3787... Generator Loss: 4.0620
Epoch 2/2... Discriminator Loss: 0.7277... Generator Loss: 1.3469
Epoch 2/2... Discriminator Loss: 0.4249... Generator Loss: 3.2944
Epoch 2/2... Discriminator Loss: 0.5624... Generator Loss: 2.1576
Epoch 2/2... Discriminator Loss: 0.3744... Generator Loss: 3.8721
Epoch 2/2... Discriminator Loss: 0.4496... Generator Loss: 3.8917
Epoch 2/2... Discriminator Loss: 0.6112... Generator Loss: 1.8756
Epoch 2/2... Discriminator Loss: 0.5202... Generator Loss: 2.6044
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-64-94cebfb8a5c9> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
---> 15           mnist_dataset.shape, mnist_dataset.image_mode)

/usr/local/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     64         if type is None:
     65             try:
---> 66                 next(self.gen)
     67             except StopIteration:
     68                 return

/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3682     finally:
   3683       if self._enforce_nesting:
-> 3684         if self.stack[-1] is not default:
   3685           raise AssertionError(
   3686               "Nesting violated for default stack of %s objects"

IndexError: list index out of range

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 10
z_dim = 50
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.7802... Generator Loss: 0.4170
Epoch 1/1... Discriminator Loss: 1.1513... Generator Loss: 0.7603
Epoch 1/1... Discriminator Loss: 0.6323... Generator Loss: 1.5460
Epoch 1/1... Discriminator Loss: 0.5878... Generator Loss: 1.7764
Epoch 1/1... Discriminator Loss: 0.3889... Generator Loss: 4.6445
Epoch 1/1... Discriminator Loss: 0.3464... Generator Loss: 9.0197
Epoch 1/1... Discriminator Loss: 1.3140... Generator Loss: 0.8648
Epoch 1/1... Discriminator Loss: 1.3065... Generator Loss: 0.7798
Epoch 1/1... Discriminator Loss: 1.2865... Generator Loss: 0.8104
Epoch 1/1... Discriminator Loss: 1.3787... Generator Loss: 0.7211
Epoch 1/1... Discriminator Loss: 1.3738... Generator Loss: 0.7257
Epoch 1/1... Discriminator Loss: 1.2098... Generator Loss: 0.7300
Epoch 1/1... Discriminator Loss: 1.4453... Generator Loss: 0.6873
Epoch 1/1... Discriminator Loss: 1.6163... Generator Loss: 0.7491
Epoch 1/1... Discriminator Loss: 1.3103... Generator Loss: 0.5049
Epoch 1/1... Discriminator Loss: 0.4786... Generator Loss: 2.6977
Epoch 1/1... Discriminator Loss: 0.4579... Generator Loss: 3.3356
Epoch 1/1... Discriminator Loss: 0.5142... Generator Loss: 3.3219
Epoch 1/1... Discriminator Loss: 0.9494... Generator Loss: 1.2949
Epoch 1/1... Discriminator Loss: 0.7041... Generator Loss: 1.6771
Epoch 1/1... Discriminator Loss: 0.4819... Generator Loss: 2.3145
Epoch 1/1... Discriminator Loss: 0.3900... Generator Loss: 3.9573
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3680       self.stack.append(default)
-> 3681       yield default
   3682     finally:

<ipython-input-13-d4f64192be58> in <module>()
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-11-6afb9865aa87> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     22         for epoch_i in range(epoch_count):
---> 23             for batch_images in get_batches(batch_size):
     24                 # Step counter

/output/helper.py in get_batches(self, batch_size)
    214                 *self.shape[1:3],
--> 215                 self.image_mode)
    216 

/output/helper.py in get_batch(image_files, width, height, mode)
     87     data_batch = np.array(
---> 88         [get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)
     89 

/output/helper.py in <listcomp>(.0)
     87     data_batch = np.array(
---> 88         [get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)
     89 

/output/helper.py in get_image(image_path, width, height, mode)
     72     """
---> 73     image = Image.open(image_path)
     74 

/usr/local/lib/python3.5/site-packages/PIL/Image.py in open(fp, mode)
   2318 
-> 2319     prefix = fp.read(16)
   2320 

KeyboardInterrupt: 

During handling of the above exception, another exception occurred:

IndexError                                Traceback (most recent call last)
<ipython-input-13-d4f64192be58> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

/usr/local/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     75                 value = type()
     76             try:
---> 77                 self.gen.throw(type, value, traceback)
     78                 raise RuntimeError("generator didn't stop after throw()")
     79             except StopIteration as exc:

/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3682     finally:
   3683       if self._enforce_nesting:
-> 3684         if self.stack[-1] is not default:
   3685           raise AssertionError(
   3686               "Nesting violated for default stack of %s objects"

IndexError: list index out of range

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.